Article

Relationship between Porcine Carcass Grades and Estimated Traits based on Conventional and Non-destructive Inspection Methods

Seok-Won LIM1, Doyon Hwang2, Sangwook Kim1, Jun-Mo Kim1,*
Author Information & Copyright
1Functional Genomics & Bioinformatics Laboratory, Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do 17546, Korea.
2Korea Institute for Animal Products Quality Evaluation, Sejong 30100, Korea.
*Corresponding Author: Jun-Mo Kim, Functional Genomics & Bioinformatics Laboratory, Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do 17546, Korea, Republic of. Phone: +82-31-670-3263. E-mail: junmokim@cau.ac.kr.

© Copyright 2021 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Nov 20, 2021; Revised: Dec 01, 2021; Accepted: Dec 02, 2021

Published Online: Dec 10, 2021

Abstract

As pork consumption increases, rapid and accurate determination of porcine carcass grades at abattoirs has become important. Non-destructive, automated inspection methods have improved slaughter efficiency in abattoirs. Furthermore, the development of a calibration equation suitable for non-destructive inspection of domestic pig breeds may lead to rapid determination of pig carcass and more objective pork grading judgement. In order to increase the efficiency of pig slaughter, the correct estimation of the automated-method that can accommodate the existing pig carcass judgement should be made. In this study, the previously developed calibration equation was verified to confirm whether the estimated traits accord with the actual measured traits of pig carcass. A total of 1,069,019 pigs, to which the developed calibration equation, was applied were used in the study and the optimal estimated regression equation for actual measured two traits (backfat thickness and hot carcass weight) was proposed using the estimated traits. The accuracy of backfat thickness and hot carcass weight traits in the estimated regression models through stepwise regression analysis was 0.840 (<italic>R</italic><sup>2</sup>) and 0.980 (<italic>R</italic><sup>2</sup>), respectively. By comparing the actually measured traits with the estimated traits, we proposed optimal estimated regression equation for the two measured traits, which we expect will be a cornerstone for the Korean porcine carcass grading system.

Keywords: Porcine carcass; Backfat thickness; Carcass weight; Meat grading; Non-destructive inspection method